Overview

Dataset statistics

Number of variables11
Number of observations8275
Missing cells18713
Missing cells (%)20.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory775.8 KiB
Average record size in memory96.0 B

Variable types

DateTime1
Numeric10

Alerts

TNX_Close is highly correlated with Dollar_Close and 7 other fieldsHigh correlation
CPI_Close is highly correlated with TNX_Close and 6 other fieldsHigh correlation
GDP_Close is highly correlated with TNX_Close and 6 other fieldsHigh correlation
GSCI_Close is highly correlated with TNX_Close and 6 other fieldsHigh correlation
GSPC_Close is highly correlated with VIX_Close and 7 other fieldsHigh correlation
VKOSPI_Close is highly correlated with VIX_Close and 7 other fieldsHigh correlation
HSI_Close is highly correlated with TNX_Close and 6 other fieldsHigh correlation
VIX_Close is highly correlated with EPU_Close and 2 other fieldsHigh correlation
Dollar_Close is highly correlated with TNX_Close and 6 other fieldsHigh correlation
EPU_Close is highly correlated with VIX_Close and 1 other fieldsHigh correlation
CPI_Close has 8022 (96.9%) missing values Missing
GDP_Close has 8204 (99.1%) missing values Missing
VKOSPI_Close has 2089 (25.2%) missing values Missing
HSI_Close has 363 (4.4%) missing values Missing
Date has unique values Unique

Reproduction

Analysis started2022-12-11 13:00:26.581049
Analysis finished2022-12-11 13:00:41.147914
Duration14.57 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct8275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size129.3 KiB
Minimum1990-01-02 00:00:00
Maximum2022-11-02 00:00:00
2022-12-11T15:00:41.259616image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-12-11T15:00:41.460904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VIX_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2506
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.65141994
Minimum9.140000343
Maximum82.69000244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:41.629452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.140000343
5-th percentile11.32699986
Q113.85000038
median17.78000069
Q323.16499996
95-th percentile33.52299995
Maximum82.69000244
Range73.5500021
Interquartile range (IQR)9.31499958

Descriptive statistics

Standard deviation8.010056527
Coefficient of variation (CV)0.4076070102
Kurtosis8.071606847
Mean19.65141994
Median Absolute Deviation (MAD)4.430000305
Skewness2.126795886
Sum162615.5
Variance64.16100557
MonotonicityNot monotonic
2022-12-11T15:00:41.787987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4200000817
 
0.2%
12.2514
 
0.2%
11.5699996914
 
0.2%
11.6499996213
 
0.2%
11.9799995413
 
0.2%
13.4200000813
 
0.2%
12.1899995813
 
0.2%
16.4099998513
 
0.2%
16.6599998512
 
0.1%
13.7899999612
 
0.1%
Other values (2496)8141
98.4%
ValueCountFrequency (%)
9.1400003431
< 0.1%
9.1499996191
< 0.1%
9.189999581
< 0.1%
9.2200002672
< 0.1%
9.310000421
< 0.1%
9.3400001531
< 0.1%
9.3599996571
< 0.1%
9.3999996191
< 0.1%
9.4200000761
< 0.1%
9.4300003052
< 0.1%
ValueCountFrequency (%)
82.690002441
< 0.1%
80.860000611
< 0.1%
80.059997561
< 0.1%
79.129997251
< 0.1%
76.449996951
< 0.1%
75.910003661
< 0.1%
75.470001221
< 0.1%
74.260002141
< 0.1%
72.669998171
< 0.1%
721
< 0.1%

TNX_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4382
Distinct (%)53.2%
Missing32
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean4.248612639
Minimum0.4990000129
Maximum9.090000153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:41.949233image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.4990000129
5-th percentile1.524100041
Q12.477499962
median4.142000198
Q35.843500137
95-th percentile7.887400007
Maximum9.090000153
Range8.59100014
Interquartile range (IQR)3.366000175

Descriptive statistics

Standard deviation2.027150914
Coefficient of variation (CV)0.4771324398
Kurtosis-0.8544459018
Mean4.248612639
Median Absolute Deviation (MAD)1.679999828
Skewness0.3009948678
Sum35021.31399
Variance4.109340829
MonotonicityNot monotonic
2022-12-11T15:00:42.096010image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.02000045811
 
0.1%
8.19999980910
 
0.1%
8.0600004210
 
0.1%
8.64999961910
 
0.1%
8.5699996959
 
0.1%
8.5100002298
 
0.1%
8.0500001918
 
0.1%
8.0699996958
 
0.1%
7.9800000198
 
0.1%
7.3299999248
 
0.1%
Other values (4372)8153
98.5%
(Missing)32
 
0.4%
ValueCountFrequency (%)
0.49900001291
< 0.1%
0.51499998571
< 0.1%
0.53600001342
< 0.1%
0.54100000861
< 0.1%
0.54299998281
< 0.1%
0.56199997661
< 0.1%
0.56300002341
< 0.1%
0.570999981
< 0.1%
0.5740000011
< 0.1%
0.57899999621
< 0.1%
ValueCountFrequency (%)
9.0900001531
 
< 0.1%
9.0799999241
 
< 0.1%
9.0699996951
 
< 0.1%
9.060000421
 
< 0.1%
9.0500001911
 
< 0.1%
9.0399999623
< 0.1%
9.0200004581
 
< 0.1%
9.0100002291
 
< 0.1%
93
< 0.1%
8.9899997711
 
< 0.1%

Dollar_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3305
Distinct (%)39.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean91.48293005
Minimum71.33000183
Maximum120.9000015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:42.240454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum71.33000183
5-th percentile76.89600067
Q183.70999908
median91.02999878
Q397.16000366
95-th percentile111.8179993
Maximum120.9000015
Range49.56999969
Interquartile range (IQR)13.45000458

Descriptive statistics

Standard deviation9.949255711
Coefficient of variation (CV)0.1087553241
Kurtosis0.1144756752
Mean91.48293005
Median Absolute Deviation (MAD)6.61000061
Skewness0.5444502489
Sum756838.2803
Variance98.98768921
MonotonicityNot monotonic
2022-12-11T15:00:42.393641image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.3199996911
 
0.1%
89.6200027511
 
0.1%
85.0500030510
 
0.1%
94.7200012210
 
0.1%
95.5800018310
 
0.1%
99.0599975610
 
0.1%
92.5199966410
 
0.1%
99.800003059
 
0.1%
96.190002449
 
0.1%
94.980003369
 
0.1%
Other values (3295)8174
98.8%
ValueCountFrequency (%)
71.330001831
< 0.1%
71.410003661
< 0.1%
71.459999081
< 0.1%
71.510002141
< 0.1%
71.569999691
< 0.1%
71.650001531
< 0.1%
71.660003662
< 0.1%
71.680000312
< 0.1%
71.800003051
< 0.1%
71.809997561
< 0.1%
ValueCountFrequency (%)
120.90000151
< 0.1%
120.23999791
< 0.1%
120.05000311
< 0.1%
120.04000091
< 0.1%
120.01000211
< 0.1%
119.90000151
< 0.1%
119.88999941
< 0.1%
119.84999851
< 0.1%
119.81999971
< 0.1%
119.79000091
< 0.1%

CPI_Close
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct247
Distinct (%)97.6%
Missing8022
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean200.9820356
Minimum128
Maximum296.761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:42.551406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile136.96
Q1162.8
median201.8
Q3237.001
95-th percentile268.4182
Maximum296.761
Range168.761
Interquartile range (IQR)74.201

Descriptive statistics

Standard deviation43.0332205
Coefficient of variation (CV)0.214114761
Kurtosis-1.025329376
Mean200.9820356
Median Absolute Deviation (MAD)35.933
Skewness0.1279987356
Sum50848.455
Variance1851.858067
MonotonicityNot monotonic
2022-12-11T15:00:42.700038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237.4982
 
< 0.1%
191.72
 
< 0.1%
134.82
 
< 0.1%
198.12
 
< 0.1%
193.72
 
< 0.1%
159.92
 
< 0.1%
227.2231
 
< 0.1%
228.5241
 
< 0.1%
228.7131
 
< 0.1%
228.8071
 
< 0.1%
Other values (237)237
 
2.9%
(Missing)8022
96.9%
ValueCountFrequency (%)
1281
< 0.1%
128.61
< 0.1%
129.11
< 0.1%
129.91
< 0.1%
131.61
< 0.1%
133.41
< 0.1%
133.71
< 0.1%
134.82
< 0.1%
135.11
< 0.1%
135.61
< 0.1%
ValueCountFrequency (%)
296.7611
< 0.1%
295.621
< 0.1%
295.3281
< 0.1%
295.2711
< 0.1%
288.6631
< 0.1%
287.7081
< 0.1%
284.1821
< 0.1%
280.1261
< 0.1%
278.5241
< 0.1%
276.591
< 0.1%

GDP_Close
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct71
Distinct (%)100.0%
Missing8204
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean13831.29038
Minimum6004.733
Maximum25663.289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:42.885255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6004.733
5-th percentile6367.6515
Q19045.398
median14381.236
Q317797.8835
95-th percentile23298.677
Maximum25663.289
Range19658.556
Interquartile range (IQR)8752.4855

Descriptive statistics

Standard deviation5445.295192
Coefficient of variation (CV)0.393693939
Kurtosis-0.8609238846
Mean13831.29038
Median Absolute Deviation (MAD)4394.223
Skewness0.3399112609
Sum982021.617
Variance29651239.73
MonotonicityNot monotonic
2022-12-11T15:00:43.058720image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6004.7331
 
< 0.1%
17462.7031
 
< 0.1%
17133.1141
 
< 0.1%
16911.0681
 
< 0.1%
16699.5511
 
< 0.1%
16420.3861
 
< 0.1%
15647.6811
 
< 0.1%
15557.5351
 
< 0.1%
15309.4711
 
< 0.1%
15141.6051
 
< 0.1%
Other values (61)61
 
0.7%
(Missing)8204
99.1%
ValueCountFrequency (%)
6004.7331
< 0.1%
6126.8621
< 0.1%
6205.9371
< 0.1%
6264.541
< 0.1%
6470.7631
< 0.1%
6566.6411
< 0.1%
6680.8031
< 0.1%
6808.9391
< 0.1%
6882.0981
< 0.1%
7013.7381
< 0.1%
ValueCountFrequency (%)
25663.2891
< 0.1%
25248.4761
< 0.1%
24349.1211
< 0.1%
23550.421
< 0.1%
23046.9341
< 0.1%
21706.5321
< 0.1%
21704.7061
< 0.1%
21531.8391
< 0.1%
21362.4281
< 0.1%
21272.4481
< 0.1%

GSCI_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7364
Distinct (%)89.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean364.5498066
Minimum128.8899994
Maximum890.289978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:43.238519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum128.8899994
5-th percentile168.3619987
Q1195.4475021
median349.3450012
Q3485.2049942
95-th percentile672.99151
Maximum890.289978
Range761.3999786
Interquartile range (IQR)289.7574921

Descriptive statistics

Standard deviation178.0738163
Coefficient of variation (CV)0.4884759587
Kurtosis-0.8693216785
Mean364.5498066
Median Absolute Deviation (MAD)151.4250031
Skewness0.558046136
Sum3016285.1
Variance31710.28403
MonotonicityNot monotonic
2022-12-11T15:00:43.434165image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.30000315
 
0.1%
177.35000615
 
0.1%
176.75999455
 
0.1%
183.88999945
 
0.1%
187.44999694
 
< 0.1%
194.66000374
 
< 0.1%
183.63000494
 
< 0.1%
175.80999764
 
< 0.1%
207.41999824
 
< 0.1%
189.27999884
 
< 0.1%
Other values (7354)8230
99.5%
ValueCountFrequency (%)
128.88999941
< 0.1%
129.05999761
< 0.1%
129.91000371
< 0.1%
129.94000241
< 0.1%
1301
< 0.1%
130.22000121
< 0.1%
130.28999331
< 0.1%
130.41000371
< 0.1%
130.66000371
< 0.1%
130.80999761
< 0.1%
ValueCountFrequency (%)
890.2899781
< 0.1%
882.85998541
< 0.1%
879.64001461
< 0.1%
877.4600221
< 0.1%
869.34002691
< 0.1%
866.94000241
< 0.1%
865.36999511
< 0.1%
865.09002691
< 0.1%
864.98999021
< 0.1%
862.80999761
< 0.1%

EPU_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6717
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.6156266
Minimum3.32
Maximum807.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:43.648557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3.32
5-th percentile27.93
Q154.665
median84.88
Q3131.78
95-th percentile246.741
Maximum807.66
Range804.34
Interquartile range (IQR)77.115

Descriptive statistics

Standard deviation76.80932832
Coefficient of variation (CV)0.7342051167
Kurtosis9.669079635
Mean104.6156266
Median Absolute Deviation (MAD)35.7
Skewness2.411436101
Sum865694.31
Variance5899.672917
MonotonicityNot monotonic
2022-12-11T15:00:43.846460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.616
 
0.1%
54.925
 
0.1%
40.145
 
0.1%
85.574
 
< 0.1%
194.684
 
< 0.1%
61.324
 
< 0.1%
54.54
 
< 0.1%
67.74
 
< 0.1%
61.094
 
< 0.1%
89.754
 
< 0.1%
Other values (6707)8231
99.5%
ValueCountFrequency (%)
3.321
< 0.1%
4.751
< 0.1%
4.811
< 0.1%
5.81
< 0.1%
5.931
< 0.1%
6.071
< 0.1%
6.161
< 0.1%
6.671
< 0.1%
7.121
< 0.1%
7.41
< 0.1%
ValueCountFrequency (%)
807.661
< 0.1%
738.021
< 0.1%
719.071
< 0.1%
690.811
< 0.1%
670.31
< 0.1%
654.181
< 0.1%
642.661
< 0.1%
638.811
< 0.1%
633.511
< 0.1%
626.031
< 0.1%

GSPC_Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8059
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491.039091
Minimum295.4599915
Maximum4796.560059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:44.081840image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum295.4599915
5-th percentile383.4110077
Q1878.3249817
median1248.48999
Q31931.005005
95-th percentile3802.383032
Maximum4796.560059
Range4501.100067
Interquartile range (IQR)1052.680023

Descriptive statistics

Standard deviation985.7783828
Coefficient of variation (CV)0.6611351699
Kurtosis1.367888136
Mean1491.039091
Median Absolute Deviation (MAD)458.8999634
Skewness1.319105286
Sum12338348.48
Variance971759.0199
MonotonicityNot monotonic
2022-12-11T15:00:44.299351image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1139.9300543
 
< 0.1%
1409.2800293
 
< 0.1%
1299.5400393
 
< 0.1%
1097.2800293
 
< 0.1%
1130.1999513
 
< 0.1%
1122.1999512
 
< 0.1%
1132.0500492
 
< 0.1%
1029.8499762
 
< 0.1%
1210.0799562
 
< 0.1%
375.22000122
 
< 0.1%
Other values (8049)8250
99.7%
ValueCountFrequency (%)
295.45999151
< 0.1%
298.76000981
< 0.1%
298.92001341
< 0.1%
300.02999881
< 0.1%
300.39001461
< 0.1%
300.97000121
< 0.1%
301.88000491
< 0.1%
303.2300111
< 0.1%
3041
< 0.1%
304.05999761
< 0.1%
ValueCountFrequency (%)
4796.5600591
< 0.1%
4793.5400391
< 0.1%
4793.0600591
< 0.1%
4791.1899411
< 0.1%
4786.3500981
< 0.1%
4778.729981
< 0.1%
4766.1801761
< 0.1%
4726.3500981
< 0.1%
4725.7900391
< 0.1%
4713.0698241
< 0.1%

VKOSPI_Close
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6068
Distinct (%)98.1%
Missing2089
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean1560.653551
Minimum280
Maximum3305.209961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:44.503791image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum280
5-th percentile525.9825134
Q1837.6924896
median1743.755005
Q32041.672485
95-th percentile2700.087402
Maximum3305.209961
Range3025.209961
Interquartile range (IQR)1203.979996

Descriptive statistics

Standard deviation717.2673904
Coefficient of variation (CV)0.4595942449
Kurtosis-0.8882975614
Mean1560.653551
Median Absolute Deviation (MAD)506.2199707
Skewness0.0552206438
Sum9654202.868
Variance514472.5093
MonotonicityNot monotonic
2022-12-11T15:00:44.730202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2066.260013
 
< 0.1%
770.95001223
 
< 0.1%
737.20001222
 
< 0.1%
2028.4499512
 
< 0.1%
766.59002692
 
< 0.1%
999.15997312
 
< 0.1%
524.2100222
 
< 0.1%
2065.0800782
 
< 0.1%
2043.760012
 
< 0.1%
699.53002932
 
< 0.1%
Other values (6058)6164
74.5%
(Missing)2089
 
25.2%
ValueCountFrequency (%)
2801
< 0.1%
288.20999151
< 0.1%
291.14999391
< 0.1%
291.92999271
< 0.1%
292.60998541
< 0.1%
297.45001221
< 0.1%
297.88000491
< 0.1%
298.54000851
< 0.1%
298.60000611
< 0.1%
300.57000731
< 0.1%
ValueCountFrequency (%)
3305.2099611
< 0.1%
3302.8400881
< 0.1%
3301.8898931
< 0.1%
3296.6799321
< 0.1%
3286.6799321
< 0.1%
3286.2199711
< 0.1%
3286.1000981
< 0.1%
3285.3400881
< 0.1%
3282.0600591
< 0.1%
3281.7800291
< 0.1%

HSI_Close
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7804
Distinct (%)98.6%
Missing363
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean16672.37908
Minimum2736.600098
Maximum33154.12109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 KiB
2022-12-11T15:00:44.915584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2736.600098
5-th percentile3975.1
Q110513.22021
median16076.71484
Q322984.96289
95-th percentile28383.85547
Maximum33154.12109
Range30417.521
Interquartile range (IQR)12471.74268

Descriptive statistics

Standard deviation7482.098165
Coefficient of variation (CV)0.4487720755
Kurtosis-1.077481641
Mean16672.37908
Median Absolute Deviation (MAD)6317.615234
Skewness-0.03656406377
Sum131911863.3
Variance55981792.95
MonotonicityNot monotonic
2022-12-11T15:00:45.111642image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30664
 
< 0.1%
30673
 
< 0.1%
29543
 
< 0.1%
33563
 
< 0.1%
30873
 
< 0.1%
30203
 
< 0.1%
40093
 
< 0.1%
29903
 
< 0.1%
30153
 
< 0.1%
10957.20023
 
< 0.1%
Other values (7794)7881
95.2%
(Missing)363
 
4.4%
ValueCountFrequency (%)
2736.6000981
< 0.1%
2738.1999511
< 0.1%
2751.6000981
< 0.1%
2751.8000491
< 0.1%
2754.8000491
< 0.1%
2756.3999021
< 0.1%
2756.6000981
< 0.1%
27601
< 0.1%
2760.8000491
< 0.1%
2762.51
< 0.1%
ValueCountFrequency (%)
33154.121091
< 0.1%
32966.890621
< 0.1%
32958.691411
< 0.1%
32930.699221
< 0.1%
32887.269531
< 0.1%
32654.449221
< 0.1%
32642.089841
< 0.1%
32607.289061
< 0.1%
32601.77931
< 0.1%
32393.410161
< 0.1%

Interactions

2022-12-11T15:00:39.447528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-12-11T15:00:29.639191image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-12-11T15:00:33.211046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2022-12-11T15:00:45.267711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-11T15:00:45.462227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-11T15:00:45.976973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-11T15:00:46.136569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-11T15:00:46.304761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-11T15:00:40.505361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-11T15:00:40.718101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-11T15:00:40.909228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-11T15:00:41.037884image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateVIX_CloseTNX_CloseDollar_CloseCPI_CloseGDP_CloseGSCI_CloseEPU_CloseGSPC_CloseVKOSPI_CloseHSI_Close
01990-01-0217.2400007.9494.290001NaNNaN212.089996169.23359.690002NaN2838.100098
11990-01-0318.1900017.9994.419998NaNNaN215.63999959.10358.760010NaN2858.699951
21990-01-0419.2199997.9892.519997NaNNaN212.13999976.38355.670013NaN2868.000000
31990-01-0520.1100017.9992.849998NaNNaN206.91999857.82352.200012NaN2839.899902
41990-01-0820.2600008.0292.050003NaNNaN199.750000126.54353.790009NaN2816.000000
51990-01-0922.2000018.0292.349998NaNNaN200.39999499.32349.619995NaN2822.000000
61990-01-1022.4400018.0392.389999NaNNaN203.71000754.72347.309998NaN2868.000000
71990-01-1120.0499998.0492.400002NaNNaN203.86000174.22348.529999NaN2855.000000
81990-01-1224.6399998.1092.430000NaNNaN202.91999855.55339.929993NaN2835.000000
91990-01-1526.340000NaN92.839996NaNNaN200.08000264.57337.000000NaN2788.600098

Last rows

DateVIX_CloseTNX_CloseDollar_CloseCPI_CloseGDP_CloseGSCI_CloseEPU_CloseGSPC_CloseVKOSPI_CloseHSI_Close
82652022-10-2029.9800004.226112.879997NaNNaN624.59997679.693665.7800292218.09008816280.219727
82662022-10-2129.6900014.213112.010002NaNNaN625.580017104.663752.7500002213.12011716211.120117
82672022-10-2429.8500004.234111.989998NaNNaN626.289978169.623797.3400882236.15991215180.690430
82682022-10-2528.4599994.108110.949997NaNNaN631.020020101.933859.1101072235.07006815165.589844
82692022-10-2627.2800014.015109.699997NaNNaN642.03997882.763830.6000982249.56005915317.669922
82702022-10-2727.3899993.937110.589996NaNNaN643.830017154.053807.3000492288.78002915427.940430
82712022-10-2825.7500004.010110.669998NaNNaN636.530029200.403901.0600592268.39990214863.059570
82722022-10-3125.8799994.077111.529999NaNNaN636.840027163.033871.9799802293.61010714687.019531
82732022-11-0125.8099994.052111.480003NaNNaN641.530029130.323856.1000982335.21997115455.269531
82742022-11-0225.8600014.059111.349998NaNNaN649.27002099.693759.6899412336.87011715827.169922